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Children can play an active and valuable role to minimise disaster risks and vulnerabilities. Yet, peer-reviewed literature on child participation in Disaster Resilience Education (DRE) is lacking. This knowledge gap is larger in low- and middle-income countries, especially related to vulnerable communities. The current study explores how child participation in developing and delivering a DRE intervention is associated with their mental well-being and resilience.

This qualitative study is part of a larger project where a DRE intervention was co-developed and delivered by children in the informal settlements in Chennai, India, using a participatory approach. This project used qualitative methods including interviews and focus group discussions with children who co-developed the intervention, their parents and staff members of the collaborating Non-Government Organisation (NGO) to understand their experiences and inform its processes.

The children involved in the development and delivery of the intervention reported that not only did they learn the skills necessary to prepare for hazards in the future, it also increased their confidence, self-worth and self-efficacy. This was also observed by parents and staff members of the collaborating NGO. They expressed pride towards the children and applauded their ability to communicate key Disaster Risk Reduction (DRR) messages with assertiveness.

There is a dearth of empirical papers on child participation in DRR activities, and this study fills some of that gap by reporting the perceived impact of children's participation on their mental well-being and resilience. Furthermore, this study can act as a roadmap for researchers aiming to do action research with children.

DRR is more effective when all stakeholders, especially the affected and at-risk children, and communities are closely involved in structuring, planning, developing and delivering key disaster preparedness messages. This study serves to show that children's participation in DRR activities not only impacts their preparedness but that it helps children in disaster recovery as well, in addition to building their resilience and overall improvement in their mental well-being.

Given the participatory nature of this study, it involves children closely in the development and delivery of DRE intervention. The communities involved in this study had complex vulnerabilities including poverty, marginalisation and based in a low-and-middle income country, India. Oftentimes, these communities are not represented in scientific literature, and this study attempts to bridge that gap.

This study presents a multi-stakeholder perspective on child participation in its potential impact on children's mental well-being and resilience. The DRE intervention was co-developed and delivered by children in the community making it unique in its development process as well as the context it was developed in – informal settlements in Chennai, India.

Existing classifications of inflationary regimes often rely on subjective judgments, hindering objectivity and accuracy. This study proposes a novel, data-driven approach to address this limitation.

We combine unsupervised clustering and classification tree methods to analyze Argentine inflation data from 1943 to 2022. Two smoothing techniques are introduced: a measure of temporal contiguity and a rolling majority rule method. The resulting regimes are compared to existing classifications based on their explanatory power for inflation-relative price variability.

Our method identifies distinct inflationary regimes, demonstrating significant improvement in objectivity and accuracy compared to existing literature. The regimes capture key historical periods and exhibit a strong association with inflation-relative price variability, providing valuable insights into Argentine inflation dynamics.

This study offers a novel methodological framework for constructing objective and accurate inflationary regimes, free from subjective biases. This approach holds potential for application to other contexts and contributes to a more nuanced understanding of inflation dynamics.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

The purpose of this study is to investigate whether audit quality is associated with the speed with which managers revise earnings forecasts to arrive at the actual earnings through the lens of the auditor selection theory. This study examines this relationship in a unique institutional setting, Japan, where nearly all managers disclose earnings forecasts.

The authors pioneer an empirical proxy to capture the speed of management forecast revisions based on well-established principles from the finance and disclosure literatures. This proxy is tested alongside other disclosure proxies (namely, accuracy, frequency and timeliness) to assess the influence of audit quality on managerial forecasting behavior.

This empirical analysis shows that forecast revision speed is higher for firms that select higher-quality auditors. While firms that select higher-quality auditors revise forecasts in a more timely fashion, these firms revise less frequently. Moreover, the authors find that the influence of audit quality on forecast revisions is asymmetric. Specifically, the analysis of downward forecast revisions shows that higher-quality auditors are associated with firms that disclose bad news via forecasts revisions faster, more frequently and in a more timely fashion. However, the analysis of upward forecast revisions shows that higher-quality auditors have no effect on the speed with which firms disclose good news via forecast revisions, even though they are associated with less frequent but more timely forecast revisions. These findings have important implications for prior studies that consistently document an asymmetric response of the stock market to good news and bad news.

The authors provide evidence on the relationship between audit quality and management earnings forecasts using a novel and intuitive measure that captures forecast revision speed. This measure speaks to the growing interest in understanding the notion of speed and timing of voluntary disclosures. This study provides a more robust and comprehensive measure of the speed with which managers revise their earnings forecasts to arrive at the actual earnings. Furthermore, this study is among the first to document an asymmetric effect of audit quality on the type of news disclosed in forecast revisions.

A generalization of Ascoli–Arzelá theorem in Banach spaces is established. Schauder's fixed point theorem is used to prove the existence of a solution for a boundary value problem of higher order. The authors’ results are obtained under, rather, general assumptions.

First, a generalization of Ascoli–Arzelá theorem in Banach spaces in Cⁿ is established. Second, this new generalization with Schauder's fixed point theorem to prove the existence of a solution for a boundary value problem of higher order is used. Finally, an illustrated example is given.

There is no funding.

In this work, a new generalization of Ascoli–Arzelá theorem in Banach spaces in Cⁿ is established. To the best of the authors’ knowledge, Ascoli–Arzelá theorem is given only in Banach spaces of continuous functions. In the second part, this new generalization with Schauder's fixed point theorem is used to prove the existence of a solution for a boundary value problem of higher order, where the derivatives appear in the non-linear terms.